In modern supply chains, stakeholders with varying degrees of autonomy and intelligence compete against each other in a constant effort to establish beneficiary contracts and maximize their own revenue. In such competitive environments, entities—software agents being a typical programming paradigm—interact in a dynamic and versatile manner, so each action can cause ripple reactions and affect the overall result. In this article, the authors argue that the utilization of data mining primitives could prove beneficial in order to analyze the supply-chain model and identify pivotal factors. They elaborate on the benefits of data mining analysis on a well-established agent supply-chain management network, both at a macro and micro level. They also analyze the results and discuss specific design choices in the context of agent performance improvement.